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OGRE: Overlap Graph-based metagenomic Read clustEring

机译:OGRE:基于图形的Metagenomic读取聚类

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Motivation: The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in large volumes of sequencing reads. A promising approach to reduce the size of metagenomic datasets is by clustering reads into groups based on their overlaps. Clustering reads are valuable to facilitate downstream analyses, including computationally intensive strain-aware assembly. As current read clustering approaches cannot handle the large datasets arising from high-throughput metagenome sequencing, a novel read clustering approach is needed. In this article, we propose OGRE, an Overlap Graph-based Read clustEring procedure for high-throughput sequencing data, with a focus on shotgun metagenomes.
机译:动机:生活在环境中的微生物可以从组合的基因组材料(也称为元基因组)中识别。对元基因组进行测序可能会导致大量测序读取。减少宏基因组数据集规模的一种有希望的方法是根据重叠将读取数据分组。聚类读取有助于下游分析,包括计算密集型应变感知组装。由于目前的读聚类方法无法处理高通量元基因组测序产生的大型数据集,因此需要一种新的读聚类方法。在本文中,我们提出了OGRE,这是一种基于重叠图的读聚类程序,用于高通量测序数据,重点关注鸟枪亚基因组。

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